欢迎来到惊蜇(SpikingJelly)的文档
SpikingJelly 是一个基于 PyTorch ,使用脉冲神经网络(Spiking Neural Network, SNN)进行深度学习的框架。
安装
注意,SpikingJelly是基于PyTorch的,需要确保环境中已经安装了PyTorch,才能安装spikingjelly。
奇数版本是开发版,随着GitHub/OpenI不断更新。偶数版本是稳定版,可以从PyPI获取。
从 PyPI 安装最新的稳定版本:
pip install spikingjelly
从源代码安装最新的开发版:
通过 GitHub:
git clone https://github.com/fangwei123456/spikingjelly.git
cd spikingjelly
python setup.py install
通过 OpenI :
git clone https://git.openi.org.cn/OpenI/spikingjelly.git
cd spikingjelly
python setup.py install
模块文档
文档索引
引用和出版物
如果您在自己的工作中用到了惊蜇(SpikingJelly),您可以按照下列格式进行引用:
@misc{SpikingJelly,
title = {SpikingJelly},
author = {Fang, Wei and Chen, Yanqi and Ding, Jianhao and Chen, Ding and Yu, Zhaofei and Zhou, Huihui and Tian, Yonghong and other contributors},
year = {2020},
howpublished = {\url{https://github.com/fangwei123456/spikingjelly}},
note = {Accessed: YYYY-MM-DD},
}
其中的 YYYY-MM-DD 需要更改为您的工作使用的惊蜇(SpikingJelly)版本对应的最后一次代码修改日期。
使用惊蜇(SpikingJelly)的出版物可见于 Publications using SpikingJelly。
项目信息
北京大学信息科学技术学院数字媒体所媒体学习组 Multimedia Learning Group 和 鹏城实验室 是SpikingJelly的主要开发者。
开发人员名单可见于 贡献者 。
友情链接
Welcome to SpikingJelly’s documentation
SpikingJelly is an open-source deep learning framework for Spiking Neural Network (SNN) based on PyTorch.
Installation
Note that SpikingJelly is based on PyTorch. Please make sure that you have installed PyTorch before you install SpikingJelly.
The odd version number is the developing version, which is updated with GitHub/OpenI repository. The even version number is the stable version and available at PyPI.
Install the last stable version from PyPI:
pip install spikingjelly
Install the latest developing version from the source codes:
From GitHub:
git clone https://github.com/fangwei123456/spikingjelly.git
cd spikingjelly
python setup.py install
From OpenI:
git clone https://git.openi.org.cn/OpenI/spikingjelly.git
cd spikingjelly
python setup.py install
- Clock_driven
- Clock driven: Neurons
- Clock driven: Encoder
- Clock driven: Use single-layer fully connected SNN to identify MNIST
- Clock driven: Use convolutional SNN to identify Fashion-MNIST
- spikingjelly.clock_driven.ann2snn
- Reinforcement Learning: Deep Q Learning
- Reinforcement Learning: Advantage Actor Critic (A2C)
- Reinforcement Learning: Proximal Policy Optimization (PPO)
- Classifying Names with a Character-level Spiking LSTM
- Propagation Pattern
- Accelerate with CUDA-Enhanced Neuron and Layer-by-Layer Propagation
- Neuromorphic Datasets Processing
- Classify DVS128 Gesture
- Recurrent Connections and Stateful Synapses
- Train Large-Scale SNN
Modules Docs
Indices and tables
Citation
If you use SpikingJelly in your work, please cite it as follows:
@misc{SpikingJelly,
title = {SpikingJelly},
author = {Fang, Wei and Chen, Yanqi and Ding, Jianhao and Chen, Ding and Yu, Zhaofei and Zhou, Huihui and Tian, Yonghong and other contributors},
year = {2020},
howpublished = {\url{https://github.com/fangwei123456/spikingjelly}},
note = {Accessed: YYYY-MM-DD},
}
Note: To specify the version of framework you are using, the default value YYYY-MM-DD in the note field should be replaced with the date of the last change of the framework you are using, i.e. the date of the latest commit.
Publications using SpikingJelly are recorded in Publications using SpikingJelly. If you use SpikingJelly in your paper, you can also add it to this table by pull request.
About
Multimedia Learning Group, Institute of Digital Media (NELVT), Peking University and Peng Cheng Laboratory are the main developers of SpikingJelly.
The list of developers can be found at contributors.
- spikingjelly.clock_driven package
- spikingjelly.datasets package
- Submodules
- spikingjelly.datasets.asl_dvs module
- spikingjelly.datasets.cifar10_dvs module
- spikingjelly.datasets.dvs128_gesture module
- spikingjelly.datasets.es_imagenet module
- spikingjelly.datasets.n_caltech101 module
- spikingjelly.datasets.n_mnist module
- spikingjelly.datasets.nav_gesture module
- spikingjelly.datasets.speechcommands module
- Module contents
play_frame()
load_matlab_mat()
load_aedat_v3()
load_ATIS_bin()
load_npz_frames()
integrate_events_segment_to_frame()
cal_fixed_frames_number_segment_index()
integrate_events_by_fixed_frames_number()
integrate_events_file_to_frames_file_by_fixed_frames_number()
integrate_events_by_fixed_duration()
integrate_events_file_to_frames_file_by_fixed_duration()
save_frames_to_npz_and_print()
create_same_directory_structure()
split_to_train_test_set()
pad_sequence_collate()
padded_sequence_mask()
NeuromorphicDatasetFolder
random_temporal_delete()
RandomTemporalDelete
create_sub_dataset()
- spikingjelly.event_driven package
- spikingjelly.visualizing package
- spikingjelly.cext package